ArticleBMC cancer2019
LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Development of a machine learning-based model to predict prognosis of resected invasive pulmonary adenocarcinoma.Journal of thoracic disease · 2025Article
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- Pooled Analysis of the Prognostic Significance of Epidermal Growth Factor Receptor (EGFR) Mutational Status in Combination with Other Driver Genomic Alterations in Stage I Resected Invasive Lung Adenocarcinoma for Recurrence-Free Survival: A Population-Based Study.Annals of surgical oncology · 2025Article
- Metabolic heterogeneity in early-stage lung adenocarcinoma revealed by RNA-seq and scRNA-seq.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review.Journal of thoracic disease · 2021Review
- A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations.Journal of thoracic disease · 2021Review
- A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Applying artificial intelligence for cancer immunotherapy.Acta pharmaceutica Sinica. B · 2021Review
- The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors.Diagnostics (Basel, Switzerland) · 2021Review
- Novel strategies in immune checkpoint inhibitor drug development: How far are we from the paradigm shift?British journal of clinical pharmacology · 2020Review
- Classify multicategory outcome in patients with lung adenocarcinoma using clinical, transcriptomic and clinico-transcriptomic data: machine learning versus multinomial models.American journal of cancer research · 2020Article
- A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features.Frontiers in genetics · 2020Article
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9 authors at 3 institutions in 1 country.
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Abstract
backgroundLung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation.
methodsWe obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis.
resultsGenes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves.
conclusionThe prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.
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